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  <div class="section" id="currently-supported-models">
<h1>Currently Supported Models<a class="headerlink" href="#currently-supported-models" title="Permalink to this headline">¶</a></h1>
<div class="section" id="arima-autoregressive-integrated-moving-average">
<h2>ARIMA (Autoregressive Integrated Moving Average)<a class="headerlink" href="#arima-autoregressive-integrated-moving-average" title="Permalink to this headline">¶</a></h2>
<p>ARIMA is a simple and widely used time series prediction model.</p>
<ul class="simple">
<li><p>Reference Paper:</p>
<ul>
<li><p><a class="reference external" href="https://www3.nd.edu/%7Ebusiforc/handouts/ARIMA%20Engineering%20Article.pdf">Williams, B. M., &amp; Hoel, L. A. (2003). Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results</a></p></li>
</ul>
</li>
<li><p>Reference Package: <code class="docutils literal notranslate"><span class="pre">pandas</span></code>, <code class="docutils literal notranslate"><span class="pre">statsmodels</span></code></p></li>
</ul>
</div>
<div class="section" id="dcrnn-diffusion-convolutional-recurrent-neural-network">
<h2>DCRNN (Diffusion Convolutional Recurrent Neural Network)<a class="headerlink" href="#dcrnn-diffusion-convolutional-recurrent-neural-network" title="Permalink to this headline">¶</a></h2>
<p>DCRNN is a deep learning framework for traffic forecasting that incorporates both spatial and temporal dependency in the traffic flow. It captures the spatial dependency using bidirectional random walks on the graph, and the temporal dependency using the encoder-decoder architecture with scheduled sampling.</p>
<ul class="simple">
<li><p>Reference Paper:</p>
<ul>
<li><p><a class="reference external" href="https://arxiv.org/abs/1707.01926">Li, Y., Yu, R., Shahabi, C., &amp; Liu, Y. (2017). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting</a></p></li>
</ul>
</li>
<li><p>Reference Implementation:</p>
<ul>
<li><p><a class="reference external" href="https://github.com/liyaguang/DCRNN">A TensorFlow implementation of Diffusion Convolutional Recurrent Neural Network (liyaguang)</a></p></li>
</ul>
</li>
</ul>
</div>
<div class="section" id="deepst-deep-learning-based-prediction-model-for-spatial-temporal-data">
<h2>DeepST (Deep learning-based prediction model for Spatial-Temporal data)<a class="headerlink" href="#deepst-deep-learning-based-prediction-model-for-spatial-temporal-data" title="Permalink to this headline">¶</a></h2>
<p>DeepST is composed of three components: 1) temporal dependent instances: describing temporal closeness, period and seasonal trend; 2) convolutional neural networks: capturing near and far spatial dependencies; 3) early and late fusions: fusing similar and different domains’ data.</p>
<ul class="simple">
<li><p>Reference Paper:</p>
<ul>
<li><p><a class="reference external" href="https://www.microsoft.com/en-us/research/wp-content/uploads/2016/09/DeepST-SIGSPATIAL2016.pdf">Zhang, J., Zheng, Y., Qi, D., Li, R., &amp; Yi, X. (2016, October). DNN-based prediction model for spatio-temporal data</a></p></li>
</ul>
</li>
</ul>
</div>
<div class="section" id="geoman-multi-level-attention-networks-for-geo-sensory-time-series-prediction">
<h2>GeoMAN (Multi-level Attention Networks for Geo-sensory Time Series Prediction)<a class="headerlink" href="#geoman-multi-level-attention-networks-for-geo-sensory-time-series-prediction" title="Permalink to this headline">¶</a></h2>
<p>GeoMAN consists of two major parts: 1) A multi-level attention mechanism (including both local and global  spatial attentions in encoder and temporal attention in decoder) to model the dynamic spatio-temporal  dependencies; 2) A general fusion module to incorporate the external factors from different domains (e.g.,  meteorology, time of day and land use).</p>
<ul class="simple">
<li><p>Reference Paper:</p>
<ul>
<li><p><a class="reference external" href="https://www.ijcai.org/proceedings/2018/0476.pdf">Liang, Y., Ke, S., Zhang, J., Yi, X., &amp; Zheng, Y. (2018, July). GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction</a></p></li>
</ul>
</li>
<li><p>Reference Implementation:</p>
<ul>
<li><p><a class="reference external" href="https://github.com/yoshall/GeoMAN">An easy implement of GeoMAN using TensorFlow (yoshall &amp; CastleLiang)</a></p></li>
</ul>
</li>
</ul>
</div>
<div class="section" id="hm-historical-mean">
<h2>HM (Historical Mean)<a class="headerlink" href="#hm-historical-mean" title="Permalink to this headline">¶</a></h2>
<p>HM is a constant model and always forecasts the sample mean of the historical data.</p>
</div>
<div class="section" id="hmm-hidden-markov-model">
<h2>HMM (Hidden Markov Model)<a class="headerlink" href="#hmm-hidden-markov-model" title="Permalink to this headline">¶</a></h2>
<p>Hidden Markov Model is a statistical Markov model in which the system being modeled is assumed to be a Markov process with hidden states. It is often used in temporal pattern recognition.</p>
<ul class="simple">
<li><p>Reference Paper:</p>
<ul>
<li><p><a class="reference external" href="https://ieeexplore.ieee.org/abstract/document/7785328">Chen, Z., Wen, J., &amp; Geng, Y. (2016, November). Predicting future traffic using hidden markov models</a></p></li>
</ul>
</li>
<li><p>Reference Package: <code class="docutils literal notranslate"><span class="pre">hmmlearn</span></code></p></li>
</ul>
</div>
<div class="section" id="st-mgcn-spatiotemporal-multi-graph-convolution-network">
<h2>ST-MGCN (Spatiotemporal Multi-graph Convolution Network)<a class="headerlink" href="#st-mgcn-spatiotemporal-multi-graph-convolution-network" title="Permalink to this headline">¶</a></h2>
<p>ST-MGCN is a deep learning based model which encoded the non-Euclidean correlations among regions using multiple graphs and explicitly captured them using multi-graph convolution.</p>
<ul class="simple">
<li><p>Reference Paper:</p>
<ul>
<li><p><a class="reference external" href="https://ieeexplore.ieee.org/abstract/document/7785328">Geng, X., Li, Y., Wang, L., Zhang, L., Yang, Q., Ye, J., &amp; Liu, Y. (2019). Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting</a></p></li>
</ul>
</li>
<li><p>Reference Implementation:</p>
<ul>
<li><p><a class="reference external" href="https://github.com/shawnwang-tech/ST-MGCN-pytorch">A PyTorch implementation of the ST-MGCN model  (shawnwang-tech)</a></p></li>
</ul>
</li>
</ul>
</div>
<div class="section" id="st-resnet">
<h2>ST-ResNet<a class="headerlink" href="#st-resnet" title="Permalink to this headline">¶</a></h2>
<p>ST-ResNet is a deep-learning model with an end-to-end structure based on unique properties of spatio-temporal data making use of convolution and residual units.</p>
<ul class="simple">
<li><p>Reference Paper:</p>
<ul>
<li><p><a class="reference external" href="https://arxiv.org/pdf/1610.00081.pdf">Zhang, J., Zheng, Y., &amp; Qi, D. (2017, February). Deep spatio-temporal residual networks for citywide crowd flows prediction</a></p></li>
</ul>
</li>
<li><p>Reference Implementation:</p>
<ul>
<li><p><a class="reference external" href="https://github.com/lucktroy/DeepST/tree/master/scripts/papers/AAAI17">Github repository (lucktroy)</a></p></li>
</ul>
</li>
</ul>
</div>
<div class="section" id="stmeta">
<h2>STMeta<a class="headerlink" href="#stmeta" title="Permalink to this headline">¶</a></h2>
<p>STMeta is our prediction model, which requires extra graph information as input, and combines Graph Convolution LSTM and Attention mechanism.</p>
<ul class="simple">
<li><p>Reference Package: <code class="docutils literal notranslate"><span class="pre">tensorflow</span></code></p></li>
</ul>
</div>
<div class="section" id="xgboost">
<h2>XGBoost<a class="headerlink" href="#xgboost" title="Permalink to this headline">¶</a></h2>
<p>XGBoost is a gradient boosting machine learning algorithm widely used in flow prediction and other machine learning prediction areas.</p>
<ul class="simple">
<li><p>Reference Paper:</p>
<ul>
<li><p><a class="reference external" href="https://www.mdpi.com/2073-8994/10/9/386">Alajali, W., Zhou, W., Wen, S., &amp; Wang, Y. (2018). Intersection Traffic Prediction Using Decision Tree Models</a></p></li>
</ul>
</li>
<li><p>Reference Package: <code class="docutils literal notranslate"><span class="pre">xgboost</span></code></p></li>
</ul>
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